Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations815
Missing cells3051
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory2.4 KiB

Variable types

Numeric9
Text4
Boolean14
Categorical16
Unsupported6

Alerts

Flag_IsDiagnosedDisorderOfBrain has constant value "False" Constant
AdmitWard is highly overall correlated with ID and 4 other fieldsHigh correlation
BMI is highly overall correlated with Weight KG CORRECTEDHigh correlation
Do you live alone? is highly overall correlated with Will there be someone, such as a family member or friend, who can support you during your initial recovery?High correlation
Flag_IsDiagnosedHeartRate is highly overall correlated with Flag_IsDiagnosedMentalandBehaviourAlcoholHigh correlation
Flag_IsDiagnosedMentalandBehaviourAlcohol is highly overall correlated with Flag_IsDiagnosedHeartRateHigh correlation
Flag_IsDiagnosedSmoker is highly overall correlated with SmokerHigh correlation
Height CM CORRECTED is highly overall correlated with Unnamed: 5High correlation
ID is highly overall correlated with AdmitWardHigh correlation
PatientTotalTime is highly overall correlated with AdmitWard and 1 other fieldsHigh correlation
PrimaryProcedureDesc is highly overall correlated with AdmitWard and 1 other fieldsHigh correlation
Smoker is highly overall correlated with Flag_IsDiagnosedSmokerHigh correlation
TheatreCode is highly overall correlated with AdmitWard and 1 other fieldsHigh correlation
TheatreName is highly overall correlated with AdmitWard and 1 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Height CM CORRECTEDHigh correlation
Weight KG CORRECTED is highly overall correlated with BMIHigh correlation
Will there be someone, such as a family member or friend, who can support you during your initial recovery? is highly overall correlated with Do you live alone?High correlation
Smoker is highly imbalanced (64.3%) Imbalance
Will there be someone, such as a family member or friend, who can support you during your initial recovery? is highly imbalanced (63.2%) Imbalance
Can you go up and down the stairs without assistance? is highly imbalanced (59.4%) Imbalance
PrimaryProcedureDesc is highly imbalanced (53.8%) Imbalance
Flag_IsDiagnosedDiabetes is highly imbalanced (54.7%) Imbalance
Flag_IsDiagnosedSmoker is highly imbalanced (71.9%) Imbalance
Flag_IsDiagnosedHeartRate is highly imbalanced (95.5%) Imbalance
Flag_IsDiagnosedMentalandBehaviourAlcohol is highly imbalanced (95.5%) Imbalance
PRIORITY_TYPE_DESCRIPTION is highly imbalanced (56.2%) Imbalance
PersonEthnicCategoryDesc is highly imbalanced (73.3%) Imbalance
Age (years): has 93 (11.4%) missing values Missing
Height (please input in centimeters) has 93 (11.4%) missing values Missing
Weight (please input in kilograms) has 93 (11.4%) missing values Missing
Height CM CORRECTED has 95 (11.7%) missing values Missing
Unnamed: 5 has 307 (37.7%) missing values Missing
Weight KG CORRECTED has 105 (12.9%) missing values Missing
BMI has 12 (1.5%) missing values Missing
Can you go up and down the stairs without assistance? has 26 (3.2%) missing values Missing
How long do you expect to stay in hospital after your operation? has 9 (1.1%) missing values Missing
PersonGender has 16 (2.0%) missing values Missing
PersonBirthDate has 16 (2.0%) missing values Missing
AdmissionDateTime has 16 (2.0%) missing values Missing
DischargeDateTime has 16 (2.0%) missing values Missing
ProfCarerOnAdmitName has 109 (13.4%) missing values Missing
PrimaryProcedureCode has 113 (13.9%) missing values Missing
PrimaryProcedureDesc has 114 (14.0%) missing values Missing
TheatreCode has 111 (13.6%) missing values Missing
TheatreName has 111 (13.6%) missing values Missing
AdmitWard has 109 (13.4%) missing values Missing
OperationStartDateTime has 111 (13.6%) missing values Missing
OperationEndDateTime has 111 (13.6%) missing values Missing
OperationLengthMinute has 111 (13.6%) missing values Missing
PatientTotalTime has 111 (13.6%) missing values Missing
LengthOfStay has 16 (2.0%) missing values Missing
Flag_IsDiagnosedDiabetes has 16 (2.0%) missing values Missing
Flag_IsDiagnosedHypertension has 16 (2.0%) missing values Missing
Flag_IsDiagnosedSmoker has 16 (2.0%) missing values Missing
Flag_IsDiagnosedHeartRate has 16 (2.0%) missing values Missing
Flag_IsDiagnosedMentalandBehaviourAlcohol has 16 (2.0%) missing values Missing
Flag_IsDiagnosedObesity has 16 (2.0%) missing values Missing
Flag_IsDiagnosedDisorderOfBrain has 16 (2.0%) missing values Missing
SWFT_LoS has 73 (9.0%) missing values Missing
PRIORITY_TYPE_DESCRIPTION has 97 (11.9%) missing values Missing
PersonEthnicCategoryDesc has 93 (11.4%) missing values Missing
Postcode has 95 (11.7%) missing values Missing
PatientClassificationDesc has 93 (11.4%) missing values Missing
Counter_PreviousOrmisActivity has 440 (54.0%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
PersonBirthDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
AdmissionDateTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
DischargeDateTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
PrimaryProcedureCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
OperationStartDateTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
OperationEndDateTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
LengthOfStay has 72 (8.8%) zeros Zeros

Reproduction

Analysis started2025-06-22 23:33:29.592935
Analysis finished2025-06-22 23:33:52.869415
Duration23.28 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408
Minimum1
Maximum815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:33:53.002395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.7
Q1204.5
median408
Q3611.5
95-th percentile774.3
Maximum815
Range814
Interquartile range (IQR)407

Descriptive statistics

Standard deviation235.41453
Coefficient of variation (CV)0.57699639
Kurtosis-1.2
Mean408
Median Absolute Deviation (MAD)204
Skewness0
Sum332520
Variance55420
MonotonicityStrictly increasing
2025-06-22T23:33:53.179067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
815 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (805) 805
98.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
815 1
0.1%
814 1
0.1%
813 1
0.1%
812 1
0.1%
811 1
0.1%
810 1
0.1%
809 1
0.1%
808 1
0.1%
807 1
0.1%
806 1
0.1%

Age (years):
Text

Missing 

Distinct82
Distinct (%)11.4%
Missing93
Missing (%)11.4%
Memory size44.8 KiB
2025-06-22T23:33:53.462129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length2
Mean length2.2520776
Min length1

Characters and Unicode

Total characters1626
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)5.3%

Sample

1st row64
2nd row50
3rd row59
4th row63
5th row75
ValueCountFrequency (%)
68 47
 
6.3%
75 38
 
5.1%
70 31
 
4.1%
77 30
 
4.0%
71 29
 
3.9%
69 28
 
3.7%
66 27
 
3.6%
63 24
 
3.2%
74 23
 
3.1%
76 23
 
3.1%
Other values (61) 448
59.9%
2025-06-22T23:33:53.843556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 330
20.3%
6 319
19.6%
5 195
12.0%
8 175
10.8%
9 83
 
5.1%
1 80
 
4.9%
0 77
 
4.7%
4 70
 
4.3%
2 63
 
3.9%
3 62
 
3.8%
Other values (20) 172
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 330
20.3%
6 319
19.6%
5 195
12.0%
8 175
10.8%
9 83
 
5.1%
1 80
 
4.9%
0 77
 
4.7%
4 70
 
4.3%
2 63
 
3.9%
3 62
 
3.8%
Other values (20) 172
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 330
20.3%
6 319
19.6%
5 195
12.0%
8 175
10.8%
9 83
 
5.1%
1 80
 
4.9%
0 77
 
4.7%
4 70
 
4.3%
2 63
 
3.9%
3 62
 
3.8%
Other values (20) 172
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 330
20.3%
6 319
19.6%
5 195
12.0%
8 175
10.8%
9 83
 
5.1%
1 80
 
4.9%
0 77
 
4.7%
4 70
 
4.3%
2 63
 
3.9%
3 62
 
3.8%
Other values (20) 172
10.6%
Distinct318
Distinct (%)44.0%
Missing93
Missing (%)11.4%
Memory size46.9 KiB
2025-06-22T23:33:54.104095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length3
Mean length4.7382271
Min length1

Characters and Unicode

Total characters3421
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique237 ?
Unique (%)32.8%

Sample

1st row178
2nd row170
3rd row150cm
4th row164
5th row182.8
ValueCountFrequency (%)
cm 36
 
4.1%
170 30
 
3.4%
178 24
 
2.7%
5ft 23
 
2.6%
5 22
 
2.5%
160 20
 
2.3%
162 18
 
2.0%
168 18
 
2.0%
163 17
 
1.9%
165 17
 
1.9%
Other values (258) 657
74.5%
2025-06-22T23:33:54.513688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 713
20.8%
5 317
 
9.3%
6 299
 
8.7%
7 283
 
8.3%
8 210
 
6.1%
. 179
 
5.2%
179
 
5.2%
c 153
 
4.5%
0 140
 
4.1%
m 139
 
4.1%
Other values (40) 809
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 713
20.8%
5 317
 
9.3%
6 299
 
8.7%
7 283
 
8.3%
8 210
 
6.1%
. 179
 
5.2%
179
 
5.2%
c 153
 
4.5%
0 140
 
4.1%
m 139
 
4.1%
Other values (40) 809
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 713
20.8%
5 317
 
9.3%
6 299
 
8.7%
7 283
 
8.3%
8 210
 
6.1%
. 179
 
5.2%
179
 
5.2%
c 153
 
4.5%
0 140
 
4.1%
m 139
 
4.1%
Other values (40) 809
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 713
20.8%
5 317
 
9.3%
6 299
 
8.7%
7 283
 
8.3%
8 210
 
6.1%
. 179
 
5.2%
179
 
5.2%
c 153
 
4.5%
0 140
 
4.1%
m 139
 
4.1%
Other values (40) 809
23.6%
Distinct388
Distinct (%)53.7%
Missing93
Missing (%)11.4%
Memory size46.2 KiB
2025-06-22T23:33:54.929370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length37
Mean length4.099723
Min length1

Characters and Unicode

Total characters2960
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)40.6%

Sample

1st row96.6kg
2nd row85
3rd row92kg
4th row73
5th row99.2
ValueCountFrequency (%)
kg 29
 
3.4%
80 18
 
2.1%
72 17
 
2.0%
73 15
 
1.8%
70 14
 
1.6%
82 13
 
1.5%
stone 13
 
1.5%
95 12
 
1.4%
kilograms 11
 
1.3%
76 11
 
1.3%
Other values (358) 704
82.1%
2025-06-22T23:33:55.527492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 249
 
8.4%
1 249
 
8.4%
7 234
 
7.9%
6 218
 
7.4%
9 196
 
6.6%
. 195
 
6.6%
0 189
 
6.4%
5 176
 
5.9%
159
 
5.4%
2 140
 
4.7%
Other values (47) 955
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 249
 
8.4%
1 249
 
8.4%
7 234
 
7.9%
6 218
 
7.4%
9 196
 
6.6%
. 195
 
6.6%
0 189
 
6.4%
5 176
 
5.9%
159
 
5.4%
2 140
 
4.7%
Other values (47) 955
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 249
 
8.4%
1 249
 
8.4%
7 234
 
7.9%
6 218
 
7.4%
9 196
 
6.6%
. 195
 
6.6%
0 189
 
6.4%
5 176
 
5.9%
159
 
5.4%
2 140
 
4.7%
Other values (47) 955
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 249
 
8.4%
1 249
 
8.4%
7 234
 
7.9%
6 218
 
7.4%
9 196
 
6.6%
. 195
 
6.6%
0 189
 
6.4%
5 176
 
5.9%
159
 
5.4%
2 140
 
4.7%
Other values (47) 955
32.3%

Height CM CORRECTED
Real number (ℝ)

High correlation  Missing 

Distinct86
Distinct (%)11.9%
Missing95
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean167.53018
Minimum61
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:33:55.677540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile152
Q1160
median167
Q3177
95-th percentile185
Maximum710
Range649
Interquartile range (IQR)17

Descriptive statistics

Standard deviation25.098625
Coefficient of variation (CV)0.14981554
Kurtosis305.87072
Mean167.53018
Median Absolute Deviation (MAD)8
Skewness13.416862
Sum120621.73
Variance629.94097
MonotonicityNot monotonic
2025-06-22T23:33:56.393648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 46
 
5.6%
160 42
 
5.2%
165 36
 
4.4%
162 31
 
3.8%
177 28
 
3.4%
167 27
 
3.3%
180 27
 
3.3%
178 27
 
3.3%
172 24
 
2.9%
163 24
 
2.9%
Other values (76) 408
50.1%
(Missing) 95
 
11.7%
ValueCountFrequency (%)
61 1
0.1%
63 1
0.1%
66 1
0.1%
67 2
0.2%
68 2
0.2%
100 1
0.1%
128 1
0.1%
137 1
0.1%
142 2
0.2%
144.78 1
0.1%
ValueCountFrequency (%)
710 1
 
0.1%
200 1
 
0.1%
196 2
 
0.2%
195 1
 
0.1%
193 2
 
0.2%
192 1
 
0.1%
191 2
 
0.2%
190.5 1
 
0.1%
190 8
1.0%
188 5
0.6%

Unnamed: 5
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)11.2%
Missing307
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean1.6755315
Minimum0
Maximum7.1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:33:56.537487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.52
Q11.6
median1.67
Q31.77
95-th percentile1.8565
Maximum7.1
Range7.1
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.29172802
Coefficient of variation (CV)0.17411074
Kurtosis239.23901
Mean1.6755315
Median Absolute Deviation (MAD)0.08
Skewness11.950809
Sum851.17
Variance0.08510524
MonotonicityNot monotonic
2025-06-22T23:33:56.691447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 39
 
4.8%
1.6 37
 
4.5%
1.65 28
 
3.4%
1.62 24
 
2.9%
1.77 24
 
2.9%
1.57 19
 
2.3%
1.67 19
 
2.3%
1.68 19
 
2.3%
1.78 19
 
2.3%
1.72 18
 
2.2%
Other values (47) 262
32.1%
(Missing) 307
37.7%
ValueCountFrequency (%)
0 1
 
0.1%
0.63 1
 
0.1%
0.66 1
 
0.1%
0.67 1
 
0.1%
0.68 2
 
0.2%
1.28 1
 
0.1%
1.37 1
 
0.1%
1.42 2
 
0.2%
1.47 3
0.4%
1.48 5
0.6%
ValueCountFrequency (%)
7.1 1
 
0.1%
2 1
 
0.1%
1.96 1
 
0.1%
1.95 1
 
0.1%
1.93 2
 
0.2%
1.92 1
 
0.1%
1.91 2
 
0.2%
1.9 5
0.6%
1.88 3
0.4%
1.87 4
0.5%

Weight KG CORRECTED
Real number (ℝ)

High correlation  Missing 

Distinct253
Distinct (%)35.6%
Missing105
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean86.631798
Minimum10.5
Maximum1032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:33:56.841172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile57.0675
Q170
median80
Q394
95-th percentile118.55
Maximum1032
Range1021.5
Interquartile range (IQR)24

Descriptive statistics

Standard deviation54.578175
Coefficient of variation (CV)0.63000165
Kurtosis181.59014
Mean86.631798
Median Absolute Deviation (MAD)12
Skewness12.248816
Sum61508.577
Variance2978.7772
MonotonicityNot monotonic
2025-06-22T23:33:57.034211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 23
 
2.8%
73 21
 
2.6%
80 19
 
2.3%
70 17
 
2.1%
82 15
 
1.8%
95 15
 
1.8%
92 13
 
1.6%
68 12
 
1.5%
69 12
 
1.5%
76 12
 
1.5%
Other values (243) 551
67.6%
(Missing) 105
 
12.9%
ValueCountFrequency (%)
10.5 1
0.1%
43.54 1
0.1%
44 1
0.1%
44.5 1
0.1%
48 1
0.1%
49.1 1
0.1%
50 2
0.2%
50.5 1
0.1%
50.8 2
0.2%
51 2
0.2%
ValueCountFrequency (%)
1032 1
0.1%
748 1
0.1%
714 1
0.1%
360 1
0.1%
207 1
0.1%
190 2
0.2%
179.63 1
0.1%
173 1
0.1%
169 1
0.1%
168 1
0.1%

BMI
Real number (ℝ)

High correlation  Missing 

Distinct591
Distinct (%)73.6%
Missing12
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean32.28766
Minimum2.2019441
Maximum335.07785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:33:57.285477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.2019441
5-th percentile21.720679
Q125.736678
median28.7
Q332.769395
95-th percentile40.997354
Maximum335.07785
Range332.87591
Interquartile range (IQR)7.0327168

Descriptive statistics

Standard deviation25.291734
Coefficient of variation (CV)0.78332507
Kurtosis87.300999
Mean32.28766
Median Absolute Deviation (MAD)3.4
Skewness8.8225559
Sum25926.991
Variance639.67178
MonotonicityNot monotonic
2025-06-22T23:33:57.531768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 15
 
1.8%
29 14
 
1.7%
28 10
 
1.2%
30 8
 
1.0%
35 8
 
1.0%
26 7
 
0.9%
33 7
 
0.9%
25 6
 
0.7%
32 6
 
0.7%
25.8 5
 
0.6%
Other values (581) 717
88.0%
(Missing) 12
 
1.5%
ValueCountFrequency (%)
2.201944059 1
0.1%
3.856749311 1
0.1%
14.87290427 1
0.1%
17.3828125 1
0.1%
17.6 1
0.1%
18 1
0.1%
18.55056787 1
0.1%
18.8 1
0.1%
19.03114187 1
0.1%
19.84375 1
0.1%
ValueCountFrequency (%)
335.0778547 1
0.1%
333.161157 1
0.1%
286.0118571 1
0.1%
281.5311077 1
0.1%
247.5607039 1
0.1%
216.2629758 1
0.1%
188.1 1
0.1%
181.4058957 1
0.1%
153.787005 1
0.1%
131.4 1
0.1%

Smoker
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size1.7 KiB
False
759 
True
 
55
(Missing)
 
1
ValueCountFrequency (%)
False 759
93.1%
True 55
 
6.7%
(Missing) 1
 
0.1%
2025-06-22T23:33:57.693242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Do you live alone?
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.4%
Memory size1.7 KiB
False
662 
True
150 
(Missing)
 
3
ValueCountFrequency (%)
False 662
81.2%
True 150
 
18.4%
(Missing) 3
 
0.4%
2025-06-22T23:33:57.775167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.2%
Memory size63.7 KiB
Yes, including overnight
729 
Yes, in the daytime only
 
43
No
 
41

Length

Max length24
Median length24
Mean length22.890529
Min length2

Characters and Unicode

Total characters18610
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes, including overnight
2nd rowYes, including overnight
3rd rowYes, in the daytime only
4th rowYes, including overnight
5th rowYes, including overnight

Common Values

ValueCountFrequency (%)
Yes, including overnight 729
89.4%
Yes, in the daytime only 43
 
5.3%
No 41
 
5.0%
(Missing) 2
 
0.2%

Length

2025-06-22T23:33:57.915125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:58.027807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 772
31.6%
including 729
29.8%
overnight 729
29.8%
in 43
 
1.8%
the 43
 
1.8%
daytime 43
 
1.8%
only 43
 
1.8%
no 41
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i 2273
12.2%
n 2273
12.2%
1630
 
8.8%
e 1587
 
8.5%
g 1458
 
7.8%
t 815
 
4.4%
o 813
 
4.4%
Y 772
 
4.1%
s 772
 
4.1%
, 772
 
4.1%
Other values (11) 5445
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2273
12.2%
n 2273
12.2%
1630
 
8.8%
e 1587
 
8.5%
g 1458
 
7.8%
t 815
 
4.4%
o 813
 
4.4%
Y 772
 
4.1%
s 772
 
4.1%
, 772
 
4.1%
Other values (11) 5445
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2273
12.2%
n 2273
12.2%
1630
 
8.8%
e 1587
 
8.5%
g 1458
 
7.8%
t 815
 
4.4%
o 813
 
4.4%
Y 772
 
4.1%
s 772
 
4.1%
, 772
 
4.1%
Other values (11) 5445
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2273
12.2%
n 2273
12.2%
1630
 
8.8%
e 1587
 
8.5%
g 1458
 
7.8%
t 815
 
4.4%
o 813
 
4.4%
Y 772
 
4.1%
s 772
 
4.1%
, 772
 
4.1%
Other values (11) 5445
29.3%
Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size1.7 KiB
True
670 
False
143 
(Missing)
 
2
ValueCountFrequency (%)
True 670
82.2%
False 143
 
17.5%
(Missing) 2
 
0.2%
2025-06-22T23:33:58.125357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing26
Missing (%)3.2%
Memory size1.7 KiB
True
725 
False
 
64
(Missing)
 
26
ValueCountFrequency (%)
True 725
89.0%
False 64
 
7.9%
(Missing) 26
 
3.2%
2025-06-22T23:33:58.213808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing6
Missing (%)0.7%
Memory size1.7 KiB
True
655 
False
154 
(Missing)
 
6
ValueCountFrequency (%)
True 655
80.4%
False 154
 
18.9%
(Missing) 6
 
0.7%
2025-06-22T23:33:58.297433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.2%
Memory size58.3 KiB
Less than a mile
438 
More than a mile
273 
In the house only
102 

Length

Max length17
Median length16
Mean length16.125461
Min length16

Characters and Unicode

Total characters13110
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMore than a mile
2nd rowLess than a mile
3rd rowLess than a mile
4th rowLess than a mile
5th rowMore than a mile

Common Values

ValueCountFrequency (%)
Less than a mile 438
53.7%
More than a mile 273
33.5%
In the house only 102
 
12.5%
(Missing) 2
 
0.2%

Length

2025-06-22T23:33:58.449767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:58.576829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
than 711
21.9%
mile 711
21.9%
a 711
21.9%
less 438
13.5%
more 273
 
8.4%
in 102
 
3.1%
the 102
 
3.1%
house 102
 
3.1%
only 102
 
3.1%

Most occurring characters

ValueCountFrequency (%)
2439
18.6%
e 1626
12.4%
a 1422
10.8%
s 978
7.5%
n 915
 
7.0%
h 915
 
7.0%
t 813
 
6.2%
l 813
 
6.2%
m 711
 
5.4%
i 711
 
5.4%
Other values (7) 1767
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2439
18.6%
e 1626
12.4%
a 1422
10.8%
s 978
7.5%
n 915
 
7.0%
h 915
 
7.0%
t 813
 
6.2%
l 813
 
6.2%
m 711
 
5.4%
i 711
 
5.4%
Other values (7) 1767
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2439
18.6%
e 1626
12.4%
a 1422
10.8%
s 978
7.5%
n 915
 
7.0%
h 915
 
7.0%
t 813
 
6.2%
l 813
 
6.2%
m 711
 
5.4%
i 711
 
5.4%
Other values (7) 1767
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2439
18.6%
e 1626
12.4%
a 1422
10.8%
s 978
7.5%
n 915
 
7.0%
h 915
 
7.0%
t 813
 
6.2%
l 813
 
6.2%
m 711
 
5.4%
i 711
 
5.4%
Other values (7) 1767
13.5%
Distinct4
Distinct (%)0.5%
Missing3
Missing (%)0.4%
Memory size58.7 KiB
No
464 
Yes - I use a walking stick or crutch
315 
Yes - I use a wheeled walker
 
22
Yes - I use a walking frame
 
11

Length

Max length37
Median length2
Mean length16.62069
Min length2

Characters and Unicode

Total characters13496
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes - I use a walking stick or crutch
5th rowNo

Common Values

ValueCountFrequency (%)
No 464
56.9%
Yes - I use a walking stick or crutch 315
38.7%
Yes - I use a wheeled walker 22
 
2.7%
Yes - I use a walking frame 11
 
1.3%
(Missing) 3
 
0.4%

Length

2025-06-22T23:33:58.725648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:58.858540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 464
13.1%
yes 348
9.9%
348
9.9%
i 348
9.9%
use 348
9.9%
a 348
9.9%
walking 326
9.2%
stick 315
8.9%
or 315
8.9%
crutch 315
8.9%
Other values (3) 55
 
1.6%

Most occurring characters

ValueCountFrequency (%)
2718
20.1%
s 1011
 
7.5%
c 945
 
7.0%
e 795
 
5.9%
o 779
 
5.8%
a 707
 
5.2%
k 663
 
4.9%
r 663
 
4.9%
u 663
 
4.9%
i 641
 
4.7%
Other values (13) 3911
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2718
20.1%
s 1011
 
7.5%
c 945
 
7.0%
e 795
 
5.9%
o 779
 
5.8%
a 707
 
5.2%
k 663
 
4.9%
r 663
 
4.9%
u 663
 
4.9%
i 641
 
4.7%
Other values (13) 3911
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2718
20.1%
s 1011
 
7.5%
c 945
 
7.0%
e 795
 
5.9%
o 779
 
5.8%
a 707
 
5.2%
k 663
 
4.9%
r 663
 
4.9%
u 663
 
4.9%
i 641
 
4.7%
Other values (13) 3911
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2718
20.1%
s 1011
 
7.5%
c 945
 
7.0%
e 795
 
5.9%
o 779
 
5.8%
a 707
 
5.2%
k 663
 
4.9%
r 663
 
4.9%
u 663
 
4.9%
i 641
 
4.7%
Other values (13) 3911
29.0%
Distinct2
Distinct (%)0.2%
Missing4
Missing (%)0.5%
Memory size1.7 KiB
False
638 
True
173 
(Missing)
 
4
ValueCountFrequency (%)
False 638
78.3%
True 173
 
21.2%
(Missing) 4
 
0.5%
2025-06-22T23:33:58.962658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct4
Distinct (%)0.5%
Missing9
Missing (%)1.1%
Memory size54.0 KiB
1 night
443 
Go home the same day
190 
2 nights
127 
3 or more nights
46 

Length

Max length20
Median length7
Mean length10.735732
Min length7

Characters and Unicode

Total characters8653
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGo home the same day
2nd rowGo home the same day
3rd row1 night
4th row1 night
5th row1 night

Common Values

ValueCountFrequency (%)
1 night 443
54.4%
Go home the same day 190
23.3%
2 nights 127
 
15.6%
3 or more nights 46
 
5.6%
(Missing) 9
 
1.1%

Length

2025-06-22T23:33:59.103144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:59.230960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 443
19.5%
night 443
19.5%
go 190
8.4%
home 190
8.4%
the 190
8.4%
same 190
8.4%
day 190
8.4%
nights 173
 
7.6%
2 127
 
5.6%
3 46
 
2.0%
Other values (2) 92
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1468
17.0%
h 996
11.5%
t 806
9.3%
g 616
 
7.1%
i 616
 
7.1%
n 616
 
7.1%
e 616
 
7.1%
o 472
 
5.5%
1 443
 
5.1%
m 426
 
4.9%
Other values (8) 1578
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1468
17.0%
h 996
11.5%
t 806
9.3%
g 616
 
7.1%
i 616
 
7.1%
n 616
 
7.1%
e 616
 
7.1%
o 472
 
5.5%
1 443
 
5.1%
m 426
 
4.9%
Other values (8) 1578
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1468
17.0%
h 996
11.5%
t 806
9.3%
g 616
 
7.1%
i 616
 
7.1%
n 616
 
7.1%
e 616
 
7.1%
o 472
 
5.5%
1 443
 
5.1%
m 426
 
4.9%
Other values (8) 1578
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1468
17.0%
h 996
11.5%
t 806
9.3%
g 616
 
7.1%
i 616
 
7.1%
n 616
 
7.1%
e 616
 
7.1%
o 472
 
5.5%
1 443
 
5.1%
m 426
 
4.9%
Other values (8) 1578
18.2%
Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
Mildly
318 
Moderately
264 
Not at all
125 
Very
108 

Length

Max length10
Median length6
Mean length7.6441718
Min length4

Characters and Unicode

Total characters6230
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot at all
2nd rowModerately
3rd rowMildly
4th rowVery
5th rowModerately

Common Values

ValueCountFrequency (%)
Mildly 318
39.0%
Moderately 264
32.4%
Not at all 125
 
15.3%
Very 108
 
13.3%

Length

2025-06-22T23:33:59.402397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:59.550039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mildly 318
29.9%
moderately 264
24.8%
not 125
 
11.7%
at 125
 
11.7%
all 125
 
11.7%
very 108
 
10.1%

Most occurring characters

ValueCountFrequency (%)
l 1150
18.5%
y 690
11.1%
e 636
10.2%
d 582
9.3%
M 582
9.3%
t 514
8.3%
a 514
8.3%
o 389
 
6.2%
r 372
 
6.0%
i 318
 
5.1%
Other values (3) 483
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1150
18.5%
y 690
11.1%
e 636
10.2%
d 582
9.3%
M 582
9.3%
t 514
8.3%
a 514
8.3%
o 389
 
6.2%
r 372
 
6.0%
i 318
 
5.1%
Other values (3) 483
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1150
18.5%
y 690
11.1%
e 636
10.2%
d 582
9.3%
M 582
9.3%
t 514
8.3%
a 514
8.3%
o 389
 
6.2%
r 372
 
6.0%
i 318
 
5.1%
Other values (3) 483
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1150
18.5%
y 690
11.1%
e 636
10.2%
d 582
9.3%
M 582
9.3%
t 514
8.3%
a 514
8.3%
o 389
 
6.2%
r 372
 
6.0%
i 318
 
5.1%
Other values (3) 483
7.8%
Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size1.7 KiB
False
540 
True
274 
(Missing)
 
1
ValueCountFrequency (%)
False 540
66.3%
True 274
33.6%
(Missing) 1
 
0.1%
2025-06-22T23:33:59.668363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.4 KiB
Less than 15 seconds
522 
More than 15 seconds
287 
Unable to do it
 
6

Length

Max length20
Median length20
Mean length19.96319
Min length15

Characters and Unicode

Total characters16270
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMore than 15 seconds
2nd rowLess than 15 seconds
3rd rowLess than 15 seconds
4th rowLess than 15 seconds
5th rowLess than 15 seconds

Common Values

ValueCountFrequency (%)
Less than 15 seconds 522
64.0%
More than 15 seconds 287
35.2%
Unable to do it 6
 
0.7%

Length

2025-06-22T23:33:59.813407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:33:59.937689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
than 809
24.8%
seconds 809
24.8%
15 809
24.8%
less 522
16.0%
more 287
 
8.8%
unable 6
 
0.2%
to 6
 
0.2%
do 6
 
0.2%
it 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s 2662
16.4%
2445
15.0%
e 1624
10.0%
n 1624
10.0%
o 1108
6.8%
t 821
 
5.0%
d 815
 
5.0%
a 815
 
5.0%
c 809
 
5.0%
h 809
 
5.0%
Other values (9) 2738
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2662
16.4%
2445
15.0%
e 1624
10.0%
n 1624
10.0%
o 1108
6.8%
t 821
 
5.0%
d 815
 
5.0%
a 815
 
5.0%
c 809
 
5.0%
h 809
 
5.0%
Other values (9) 2738
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2662
16.4%
2445
15.0%
e 1624
10.0%
n 1624
10.0%
o 1108
6.8%
t 821
 
5.0%
d 815
 
5.0%
a 815
 
5.0%
c 809
 
5.0%
h 809
 
5.0%
Other values (9) 2738
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2662
16.4%
2445
15.0%
e 1624
10.0%
n 1624
10.0%
o 1108
6.8%
t 821
 
5.0%
d 815
 
5.0%
a 815
 
5.0%
c 809
 
5.0%
h 809
 
5.0%
Other values (9) 2738
16.8%

PersonGender
Categorical

Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size49.6 KiB
Female
456 
Male
343 

Length

Max length6
Median length6
Mean length5.1414268
Min length4

Characters and Unicode

Total characters4108
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 456
56.0%
Male 343
42.1%
(Missing) 16
 
2.0%

Length

2025-06-22T23:34:00.125109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:00.255625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 456
57.1%
male 343
42.9%

Most occurring characters

ValueCountFrequency (%)
e 1255
30.6%
a 799
19.4%
l 799
19.4%
F 456
 
11.1%
m 456
 
11.1%
M 343
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1255
30.6%
a 799
19.4%
l 799
19.4%
F 456
 
11.1%
m 456
 
11.1%
M 343
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1255
30.6%
a 799
19.4%
l 799
19.4%
F 456
 
11.1%
m 456
 
11.1%
M 343
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1255
30.6%
a 799
19.4%
l 799
19.4%
F 456
 
11.1%
m 456
 
11.1%
M 343
 
8.3%

PersonBirthDate
Unsupported

Missing  Rejected  Unsupported 

Missing16
Missing (%)2.0%
Memory size40.1 KiB

AdmissionDateTime
Unsupported

Missing  Rejected  Unsupported 

Missing16
Missing (%)2.0%
Memory size39.3 KiB

DischargeDateTime
Unsupported

Missing  Rejected  Unsupported 

Missing16
Missing (%)2.0%
Memory size39.3 KiB

ProfCarerOnAdmitName
Categorical

Missing 

Distinct13
Distinct (%)1.8%
Missing109
Missing (%)13.4%
Memory size50.8 KiB
Faisal
140 
Shepherd
133 
Robertson
116 
Shah
99 
Ud-Din
80 
Other values (8)
138 

Length

Max length10
Median length9
Mean length6.5708215
Min length4

Characters and Unicode

Total characters4639
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowShah
2nd rowRobertson
3rd rowFaisal
4th rowShah
5th rowRobertson

Common Values

ValueCountFrequency (%)
Faisal 140
17.2%
Shepherd 133
16.3%
Robertson 116
14.2%
Shah 99
12.1%
Ud-Din 80
9.8%
Waite 73
9.0%
Abdel Kawy 21
 
2.6%
Wood 20
 
2.5%
Krishnan 11
 
1.3%
Young 6
 
0.7%
Other values (3) 7
 
0.9%
(Missing) 109
13.4%

Length

2025-06-22T23:34:00.399223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
faisal 140
19.3%
shepherd 133
18.3%
robertson 116
16.0%
shah 99
13.6%
ud-din 80
11.0%
waite 73
10.0%
abdel 21
 
2.9%
kawy 21
 
2.9%
wood 20
 
2.8%
krishnan 11
 
1.5%
Other values (4) 13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
a 493
 
10.6%
h 477
 
10.3%
e 476
 
10.3%
i 304
 
6.6%
o 283
 
6.1%
s 267
 
5.8%
r 265
 
5.7%
d 254
 
5.5%
S 232
 
5.0%
n 225
 
4.9%
Other values (21) 1363
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 493
 
10.6%
h 477
 
10.3%
e 476
 
10.3%
i 304
 
6.6%
o 283
 
6.1%
s 267
 
5.8%
r 265
 
5.7%
d 254
 
5.5%
S 232
 
5.0%
n 225
 
4.9%
Other values (21) 1363
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 493
 
10.6%
h 477
 
10.3%
e 476
 
10.3%
i 304
 
6.6%
o 283
 
6.1%
s 267
 
5.8%
r 265
 
5.7%
d 254
 
5.5%
S 232
 
5.0%
n 225
 
4.9%
Other values (21) 1363
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 493
 
10.6%
h 477
 
10.3%
e 476
 
10.3%
i 304
 
6.6%
o 283
 
6.1%
s 267
 
5.8%
r 265
 
5.7%
d 254
 
5.5%
S 232
 
5.0%
n 225
 
4.9%
Other values (21) 1363
29.4%

PrimaryProcedureCode
Unsupported

Missing  Rejected  Unsupported 

Missing113
Missing (%)13.9%
Memory size45.5 KiB

PrimaryProcedureDesc
Categorical

High correlation  Imbalance  Missing 

Distinct24
Distinct (%)3.4%
Missing114
Missing (%)14.0%
Memory size88.5 KiB
Primary total prosthetic replacement of hip joint not using cement
316 
Primary total prosthetic replacement of knee joint using cement
212 
Primary resurfacing arthroplasty of joint
105 
Primary hybrid prosthetic replacement of hip joint using cemented femoral component
 
21
Conversion to total prosthetic replacement of knee joint using cement
 
11
Other values (19)
36 

Length

Max length86
Median length83
Mean length61.721826
Min length21

Characters and Unicode

Total characters43267
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.6%

Sample

1st rowPrimary total prosthetic replacement of hip joint not using cement
2nd rowPrimary total prosthetic replacement of knee joint using cement
3rd rowPrimary total prosthetic replacement of hip joint not using cement
4th rowPrimary total prosthetic replacement of hip joint not using cement
5th rowPrimary total prosthetic replacement of knee joint using cement

Common Values

ValueCountFrequency (%)
Primary total prosthetic replacement of hip joint not using cement 316
38.8%
Primary total prosthetic replacement of knee joint using cement 212
26.0%
Primary resurfacing arthroplasty of joint 105
 
12.9%
Primary hybrid prosthetic replacement of hip joint using cemented femoral component 21
 
2.6%
Conversion to total prosthetic replacement of knee joint using cement 11
 
1.3%
Revision of total prosthetic replacement of hip joint not using cement 7
 
0.9%
Revision of total prosthetic replacement of knee joint using cement 3
 
0.4%
Primary total prosthetic replacement of knee joint not using cement 3
 
0.4%
Primary hybrid prosthetic replacement of hip joint using cemented acetabular component 3
 
0.4%
Primary total prosthetic replacement of hip joint using cement 3
 
0.4%
Other values (14) 17
 
2.1%
(Missing) 114
 
14.0%

Length

2025-06-22T23:34:00.638752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 717
11.4%
joint 694
11.1%
primary 667
10.6%
prosthetic 588
9.4%
replacement 588
9.4%
using 586
9.4%
cement 562
9.0%
total 559
8.9%
hip 356
5.7%
not 330
5.3%
Other values (30) 618
9.9%

Most occurring characters

ValueCountFrequency (%)
5564
12.9%
t 4767
11.0%
e 4214
9.7%
n 3236
 
7.5%
o 3135
 
7.2%
i 3084
 
7.1%
r 3013
 
7.0%
a 2182
 
5.0%
c 1909
 
4.4%
m 1891
 
4.4%
Other values (20) 10272
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5564
12.9%
t 4767
11.0%
e 4214
9.7%
n 3236
 
7.5%
o 3135
 
7.2%
i 3084
 
7.1%
r 3013
 
7.0%
a 2182
 
5.0%
c 1909
 
4.4%
m 1891
 
4.4%
Other values (20) 10272
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5564
12.9%
t 4767
11.0%
e 4214
9.7%
n 3236
 
7.5%
o 3135
 
7.2%
i 3084
 
7.1%
r 3013
 
7.0%
a 2182
 
5.0%
c 1909
 
4.4%
m 1891
 
4.4%
Other values (20) 10272
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5564
12.9%
t 4767
11.0%
e 4214
9.7%
n 3236
 
7.5%
o 3135
 
7.2%
i 3084
 
7.1%
r 3013
 
7.0%
a 2182
 
5.0%
c 1909
 
4.4%
m 1891
 
4.4%
Other values (20) 10272
23.7%

TheatreCode
Categorical

High correlation  Missing 

Distinct7
Distinct (%)1.0%
Missing111
Missing (%)13.6%
Memory size49.3 KiB
THE 6
305 
VAN1
157 
THE5
144 
THE4
59 
THE3
36 
Other values (2)
 
3

Length

Max length5
Median length4
Mean length4.4375
Min length4

Characters and Unicode

Total characters3124
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTHE 6
2nd rowTHE5
3rd rowTHE5
4th rowTHE 6
5th rowTHE 6

Common Values

ValueCountFrequency (%)
THE 6 305
37.4%
VAN1 157
19.3%
THE5 144
17.7%
THE4 59
 
7.2%
THE3 36
 
4.4%
DSC D 2
 
0.2%
DSC A 1
 
0.1%
(Missing) 111
 
13.6%

Length

2025-06-22T23:34:00.839151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:01.018137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
the 305
30.1%
6 305
30.1%
van1 157
15.5%
the5 144
14.2%
the4 59
 
5.8%
the3 36
 
3.6%
dsc 3
 
0.3%
d 2
 
0.2%
a 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 544
17.4%
H 544
17.4%
E 544
17.4%
308
9.9%
6 305
9.8%
A 158
 
5.1%
V 157
 
5.0%
N 157
 
5.0%
1 157
 
5.0%
5 144
 
4.6%
Other values (5) 106
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 544
17.4%
H 544
17.4%
E 544
17.4%
308
9.9%
6 305
9.8%
A 158
 
5.1%
V 157
 
5.0%
N 157
 
5.0%
1 157
 
5.0%
5 144
 
4.6%
Other values (5) 106
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 544
17.4%
H 544
17.4%
E 544
17.4%
308
9.9%
6 305
9.8%
A 158
 
5.1%
V 157
 
5.0%
N 157
 
5.0%
1 157
 
5.0%
5 144
 
4.6%
Other values (5) 106
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 544
17.4%
H 544
17.4%
E 544
17.4%
308
9.9%
6 305
9.8%
A 158
 
5.1%
V 157
 
5.0%
N 157
 
5.0%
1 157
 
5.0%
5 144
 
4.6%
Other values (5) 106
 
3.4%

TheatreName
Categorical

High correlation  Missing 

Distinct7
Distinct (%)1.0%
Missing111
Missing (%)13.6%
Memory size52.9 KiB
THEATRE 6
305 
VANGUARD ONE
157 
Theatre 5
144 
Theatre 4
59 
Theatre 3
36 
Other values (2)
 
3

Length

Max length12
Median length9
Mean length9.6690341
Min length9

Characters and Unicode

Total characters6807
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTHEATRE 6
2nd rowTheatre 5
3rd rowTheatre 5
4th rowTHEATRE 6
5th rowTHEATRE 6

Common Values

ValueCountFrequency (%)
THEATRE 6 305
37.4%
VANGUARD ONE 157
19.3%
Theatre 5 144
17.7%
Theatre 4 59
 
7.2%
Theatre 3 36
 
4.4%
THEATRE D 2
 
0.2%
THEATRE A 1
 
0.1%
(Missing) 111
 
13.6%

Length

2025-06-22T23:34:01.175030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:01.280464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
theatre 547
38.8%
6 305
21.7%
vanguard 157
 
11.2%
one 157
 
11.2%
5 144
 
10.2%
4 59
 
4.2%
3 36
 
2.6%
d 2
 
0.1%
a 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 855
12.6%
E 773
11.4%
704
10.3%
A 623
 
9.2%
e 478
 
7.0%
R 465
 
6.8%
N 314
 
4.6%
H 308
 
4.5%
6 305
 
4.5%
t 239
 
3.5%
Other values (11) 1743
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 855
12.6%
E 773
11.4%
704
10.3%
A 623
 
9.2%
e 478
 
7.0%
R 465
 
6.8%
N 314
 
4.6%
H 308
 
4.5%
6 305
 
4.5%
t 239
 
3.5%
Other values (11) 1743
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 855
12.6%
E 773
11.4%
704
10.3%
A 623
 
9.2%
e 478
 
7.0%
R 465
 
6.8%
N 314
 
4.6%
H 308
 
4.5%
6 305
 
4.5%
t 239
 
3.5%
Other values (11) 1743
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 855
12.6%
E 773
11.4%
704
10.3%
A 623
 
9.2%
e 478
 
7.0%
R 465
 
6.8%
N 314
 
4.6%
H 308
 
4.5%
6 305
 
4.5%
t 239
 
3.5%
Other values (11) 1743
25.6%

AdmitWard
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.6%
Missing109
Missing (%)13.4%
Memory size54.6 KiB
GREVILLE WARD
384 
THOMAS WARD
297 
FAIRFAX FAA
 
21
DAY SURGERY CENTRE
 
4

Length

Max length18
Median length13
Mean length12.127479
Min length11

Characters and Unicode

Total characters8562
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGREVILLE WARD
2nd rowGREVILLE WARD
3rd rowGREVILLE WARD
4th rowGREVILLE WARD
5th rowGREVILLE WARD

Common Values

ValueCountFrequency (%)
GREVILLE WARD 384
47.1%
THOMAS WARD 297
36.4%
FAIRFAX FAA 21
 
2.6%
DAY SURGERY CENTRE 4
 
0.5%
(Missing) 109
 
13.4%

Length

2025-06-22T23:34:01.425119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:01.515687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ward 681
48.1%
greville 384
27.1%
thomas 297
21.0%
fairfax 21
 
1.5%
faa 21
 
1.5%
day 4
 
0.3%
surgery 4
 
0.3%
centre 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R 1098
12.8%
A 1066
12.5%
E 780
9.1%
L 768
9.0%
710
8.3%
D 685
8.0%
W 681
8.0%
I 405
 
4.7%
G 388
 
4.5%
V 384
 
4.5%
Other values (11) 1597
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1098
12.8%
A 1066
12.5%
E 780
9.1%
L 768
9.0%
710
8.3%
D 685
8.0%
W 681
8.0%
I 405
 
4.7%
G 388
 
4.5%
V 384
 
4.5%
Other values (11) 1597
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1098
12.8%
A 1066
12.5%
E 780
9.1%
L 768
9.0%
710
8.3%
D 685
8.0%
W 681
8.0%
I 405
 
4.7%
G 388
 
4.5%
V 384
 
4.5%
Other values (11) 1597
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1098
12.8%
A 1066
12.5%
E 780
9.1%
L 768
9.0%
710
8.3%
D 685
8.0%
W 681
8.0%
I 405
 
4.7%
G 388
 
4.5%
V 384
 
4.5%
Other values (11) 1597
18.7%

OperationStartDateTime
Unsupported

Missing  Rejected  Unsupported 

Missing111
Missing (%)13.6%
Memory size37.1 KiB

OperationEndDateTime
Unsupported

Missing  Rejected  Unsupported 

Missing111
Missing (%)13.6%
Memory size37.1 KiB

OperationLengthMinute
Real number (ℝ)

Missing 

Distinct104
Distinct (%)14.8%
Missing111
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean86.237216
Minimum12
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:34:01.639296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile58
Q171
median84
Q397.25
95-th percentile125
Maximum187
Range175
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation21.93996
Coefficient of variation (CV)0.25441406
Kurtosis2.3931448
Mean86.237216
Median Absolute Deviation (MAD)13
Skewness0.96455905
Sum60711
Variance481.36186
MonotonicityNot monotonic
2025-06-22T23:34:01.800658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 20
 
2.5%
75 20
 
2.5%
84 19
 
2.3%
82 18
 
2.2%
73 18
 
2.2%
74 17
 
2.1%
66 17
 
2.1%
94 16
 
2.0%
69 16
 
2.0%
92 16
 
2.0%
Other values (94) 527
64.7%
(Missing) 111
 
13.6%
ValueCountFrequency (%)
12 1
 
0.1%
14 1
 
0.1%
33 1
 
0.1%
40 1
 
0.1%
41 1
 
0.1%
49 2
0.2%
50 4
0.5%
51 4
0.5%
52 3
0.4%
53 4
0.5%
ValueCountFrequency (%)
187 2
0.2%
183 1
0.1%
174 1
0.1%
159 2
0.2%
157 2
0.2%
153 1
0.1%
152 1
0.1%
150 1
0.1%
147 1
0.1%
146 2
0.2%

PatientTotalTime
Real number (ℝ)

High correlation  Missing 

Distinct46
Distinct (%)6.5%
Missing111
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean131.91335
Minimum30
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:34:01.953162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile117
Q1120
median120
Q3134
95-th percentile210
Maximum270
Range240
Interquartile range (IQR)14

Descriptive statistics

Standard deviation29.929023
Coefficient of variation (CV)0.22688395
Kurtosis6.9109283
Mean131.91335
Median Absolute Deviation (MAD)1
Skewness2.5395737
Sum92867
Variance895.74639
MonotonicityNot monotonic
2025-06-22T23:34:02.096731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
120 350
42.9%
121 50
 
6.1%
134 42
 
5.2%
240 28
 
3.4%
135 26
 
3.2%
141 23
 
2.8%
128 15
 
1.8%
180 14
 
1.7%
118 14
 
1.7%
136 13
 
1.6%
Other values (36) 129
 
15.8%
(Missing) 111
 
13.6%
ValueCountFrequency (%)
30 1
 
0.1%
40 1
 
0.1%
60 2
 
0.2%
90 1
 
0.1%
109 4
0.5%
110 6
0.7%
111 2
 
0.2%
112 2
 
0.2%
114 8
1.0%
115 5
0.6%
ValueCountFrequency (%)
270 1
 
0.1%
250 2
 
0.2%
240 28
3.4%
220 4
 
0.5%
210 7
 
0.9%
200 5
 
0.6%
194 1
 
0.1%
190 1
 
0.1%
187 1
 
0.1%
180 14
1.7%

LengthOfStay
Real number (ℝ)

Missing  Zeros 

Distinct13
Distinct (%)1.6%
Missing16
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1.5193992
Minimum0
Maximum30
Zeros72
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:34:02.206486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6631979
Coefficient of variation (CV)1.0946418
Kurtosis115.98009
Mean1.5193992
Median Absolute Deviation (MAD)0
Skewness8.1839098
Sum1214
Variance2.7662272
MonotonicityNot monotonic
2025-06-22T23:34:02.316856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 466
57.2%
2 176
 
21.6%
0 72
 
8.8%
3 43
 
5.3%
4 15
 
1.8%
5 12
 
1.5%
6 3
 
0.4%
9 3
 
0.4%
7 3
 
0.4%
8 3
 
0.4%
Other values (3) 3
 
0.4%
(Missing) 16
 
2.0%
ValueCountFrequency (%)
0 72
 
8.8%
1 466
57.2%
2 176
 
21.6%
3 43
 
5.3%
4 15
 
1.8%
5 12
 
1.5%
6 3
 
0.4%
7 3
 
0.4%
8 3
 
0.4%
9 3
 
0.4%
ValueCountFrequency (%)
30 1
 
0.1%
14 1
 
0.1%
13 1
 
0.1%
9 3
 
0.4%
8 3
 
0.4%
7 3
 
0.4%
6 3
 
0.4%
5 12
 
1.5%
4 15
 
1.8%
3 43
5.3%

Flag_IsDiagnosedDiabetes
Boolean

Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
723 
True
76 
(Missing)
 
16
ValueCountFrequency (%)
False 723
88.7%
True 76
 
9.3%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.402446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
425 
True
374 
(Missing)
 
16
ValueCountFrequency (%)
False 425
52.1%
True 374
45.9%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.458737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Flag_IsDiagnosedSmoker
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
760 
True
 
39
(Missing)
 
16
ValueCountFrequency (%)
False 760
93.3%
True 39
 
4.8%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.510846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Flag_IsDiagnosedHeartRate
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
795 
True
 
4
(Missing)
 
16
ValueCountFrequency (%)
False 795
97.5%
True 4
 
0.5%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.565263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Flag_IsDiagnosedMentalandBehaviourAlcohol
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
795 
True
 
4
(Missing)
 
16
ValueCountFrequency (%)
False 795
97.5%
True 4
 
0.5%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.617894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Flag_IsDiagnosedObesity
Boolean

Missing 

Distinct2
Distinct (%)0.3%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
703 
True
96 
(Missing)
 
16
ValueCountFrequency (%)
False 703
86.3%
True 96
 
11.8%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.666782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Flag_IsDiagnosedDisorderOfBrain
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing16
Missing (%)2.0%
Memory size1.7 KiB
False
799 
(Missing)
 
16
ValueCountFrequency (%)
False 799
98.0%
(Missing) 16
 
2.0%
2025-06-22T23:34:02.719655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

SWFT_LoS
Categorical

Missing 

Distinct5
Distinct (%)0.7%
Missing73
Missing (%)9.0%
Memory size48.2 KiB
1.0
515 
0.0
155 
2.0
66 
3.0
 
5
10.0
 
1

Length

Max length4
Median length3
Mean length3.0013477
Min length3

Characters and Unicode

Total characters2227
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 515
63.2%
0.0 155
 
19.0%
2.0 66
 
8.1%
3.0 5
 
0.6%
10.0 1
 
0.1%
(Missing) 73
 
9.0%

Length

2025-06-22T23:34:02.825587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:02.920951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 515
69.4%
0.0 155
 
20.9%
2.0 66
 
8.9%
3.0 5
 
0.7%
10.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 898
40.3%
. 742
33.3%
1 516
23.2%
2 66
 
3.0%
3 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 898
40.3%
. 742
33.3%
1 516
23.2%
2 66
 
3.0%
3 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 898
40.3%
. 742
33.3%
1 516
23.2%
2 66
 
3.0%
3 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 898
40.3%
. 742
33.3%
1 516
23.2%
2 66
 
3.0%
3 5
 
0.2%

PRIORITY_TYPE_DESCRIPTION
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing97
Missing (%)11.9%
Memory size51.0 KiB
Routine
653 
Urgent
 
65

Length

Max length7
Median length7
Mean length6.9094708
Min length6

Characters and Unicode

Total characters4961
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoutine
2nd rowRoutine
3rd rowRoutine
4th rowRoutine
5th rowRoutine

Common Values

ValueCountFrequency (%)
Routine 653
80.1%
Urgent 65
 
8.0%
(Missing) 97
 
11.9%

Length

2025-06-22T23:34:03.034602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:03.111212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
routine 653
90.9%
urgent 65
 
9.1%

Most occurring characters

ValueCountFrequency (%)
n 718
14.5%
t 718
14.5%
e 718
14.5%
R 653
13.2%
o 653
13.2%
u 653
13.2%
i 653
13.2%
U 65
 
1.3%
r 65
 
1.3%
g 65
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 718
14.5%
t 718
14.5%
e 718
14.5%
R 653
13.2%
o 653
13.2%
u 653
13.2%
i 653
13.2%
U 65
 
1.3%
r 65
 
1.3%
g 65
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 718
14.5%
t 718
14.5%
e 718
14.5%
R 653
13.2%
o 653
13.2%
u 653
13.2%
i 653
13.2%
U 65
 
1.3%
r 65
 
1.3%
g 65
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 718
14.5%
t 718
14.5%
e 718
14.5%
R 653
13.2%
o 653
13.2%
u 653
13.2%
i 653
13.2%
U 65
 
1.3%
r 65
 
1.3%
g 65
 
1.3%

PersonEthnicCategoryDesc
Categorical

Imbalance  Missing 

Distinct12
Distinct (%)1.7%
Missing93
Missing (%)11.4%
Memory size51.8 KiB
British
608 
Not stated
61 
Any other white background
 
27
Indian
 
9
Any other ethnic group
 
4
Other values (7)
 
13

Length

Max length26
Median length7
Mean length8.0567867
Min length5

Characters and Unicode

Total characters5817
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.6%

Sample

1st rowBritish
2nd rowBritish
3rd rowBritish
4th rowBritish
5th rowBritish

Common Values

ValueCountFrequency (%)
British 608
74.6%
Not stated 61
 
7.5%
Any other white background 27
 
3.3%
Indian 9
 
1.1%
Any other ethnic group 4
 
0.5%
Pakistani 4
 
0.5%
Irish 3
 
0.4%
Not known 2
 
0.2%
White and Asian 1
 
0.1%
Chinese 1
 
0.1%
Other values (2) 2
 
0.2%
(Missing) 93
 
11.4%

Length

2025-06-22T23:34:03.202175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 608
69.1%
not 63
 
7.2%
stated 61
 
6.9%
any 31
 
3.5%
other 31
 
3.5%
white 28
 
3.2%
background 27
 
3.1%
indian 9
 
1.0%
ethnic 4
 
0.5%
group 4
 
0.5%
Other values (8) 14
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 1272
21.9%
t 860
14.8%
s 678
11.7%
h 675
11.6%
r 675
11.6%
B 608
10.5%
158
 
2.7%
e 127
 
2.2%
o 127
 
2.2%
a 110
 
1.9%
Other values (17) 527
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1272
21.9%
t 860
14.8%
s 678
11.7%
h 675
11.6%
r 675
11.6%
B 608
10.5%
158
 
2.7%
e 127
 
2.2%
o 127
 
2.2%
a 110
 
1.9%
Other values (17) 527
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1272
21.9%
t 860
14.8%
s 678
11.7%
h 675
11.6%
r 675
11.6%
B 608
10.5%
158
 
2.7%
e 127
 
2.2%
o 127
 
2.2%
a 110
 
1.9%
Other values (17) 527
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1272
21.9%
t 860
14.8%
s 678
11.7%
h 675
11.6%
r 675
11.6%
B 608
10.5%
158
 
2.7%
e 127
 
2.2%
o 127
 
2.2%
a 110
 
1.9%
Other values (17) 527
9.1%

Postcode
Text

Missing 

Distinct445
Distinct (%)61.8%
Missing95
Missing (%)11.7%
Memory size48.5 KiB
2025-06-22T23:34:03.557785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6152778
Min length7

Characters and Unicode

Total characters5483
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)38.3%

Sample

1st rowCV37 0AA
2nd rowCV35 8PA
3rd rowCV3 5GA
4th rowB95 5HA
5th rowCV31 3LA
ValueCountFrequency (%)
cv37 99
 
6.9%
cv35 60
 
4.2%
cv8 52
 
3.6%
cv31 51
 
3.5%
cv47 50
 
3.5%
cv34 50
 
3.5%
cv32 36
 
2.5%
cv3 28
 
1.9%
cv5 27
 
1.9%
b49 23
 
1.6%
Other values (210) 964
66.9%
2025-06-22T23:34:04.064787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 787
14.4%
720
13.1%
C 579
10.6%
V 579
10.6%
3 436
 
8.0%
7 270
 
4.9%
5 202
 
3.7%
4 191
 
3.5%
1 179
 
3.3%
9 179
 
3.3%
Other values (24) 1361
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 787
14.4%
720
13.1%
C 579
10.6%
V 579
10.6%
3 436
 
8.0%
7 270
 
4.9%
5 202
 
3.7%
4 191
 
3.5%
1 179
 
3.3%
9 179
 
3.3%
Other values (24) 1361
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 787
14.4%
720
13.1%
C 579
10.6%
V 579
10.6%
3 436
 
8.0%
7 270
 
4.9%
5 202
 
3.7%
4 191
 
3.5%
1 179
 
3.3%
9 179
 
3.3%
Other values (24) 1361
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 787
14.4%
720
13.1%
C 579
10.6%
V 579
10.6%
3 436
 
8.0%
7 270
 
4.9%
5 202
 
3.7%
4 191
 
3.5%
1 179
 
3.3%
9 179
 
3.3%
Other values (24) 1361
24.8%

PatientClassificationDesc
Categorical

Missing 

Distinct2
Distinct (%)0.3%
Missing93
Missing (%)11.4%
Memory size59.5 KiB
Ordinary admission.
636 
Day case admission.
86 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13718
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrdinary admission.
2nd rowOrdinary admission.
3rd rowOrdinary admission.
4th rowOrdinary admission.
5th rowOrdinary admission.

Common Values

ValueCountFrequency (%)
Ordinary admission. 636
78.0%
Day case admission. 86
 
10.6%
(Missing) 93
 
11.4%

Length

2025-06-22T23:34:04.889819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T23:34:04.968352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
admission 722
47.2%
ordinary 636
41.6%
day 86
 
5.6%
case 86
 
5.6%

Most occurring characters

ValueCountFrequency (%)
i 2080
15.2%
a 1530
11.2%
s 1530
11.2%
n 1358
9.9%
d 1358
9.9%
r 1272
9.3%
808
 
5.9%
. 722
 
5.3%
y 722
 
5.3%
m 722
 
5.3%
Other values (5) 1616
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2080
15.2%
a 1530
11.2%
s 1530
11.2%
n 1358
9.9%
d 1358
9.9%
r 1272
9.3%
808
 
5.9%
. 722
 
5.3%
y 722
 
5.3%
m 722
 
5.3%
Other values (5) 1616
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2080
15.2%
a 1530
11.2%
s 1530
11.2%
n 1358
9.9%
d 1358
9.9%
r 1272
9.3%
808
 
5.9%
. 722
 
5.3%
y 722
 
5.3%
m 722
 
5.3%
Other values (5) 1616
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2080
15.2%
a 1530
11.2%
s 1530
11.2%
n 1358
9.9%
d 1358
9.9%
r 1272
9.3%
808
 
5.9%
. 722
 
5.3%
y 722
 
5.3%
m 722
 
5.3%
Other values (5) 1616
11.8%

Counter_PreviousOrmisActivity
Real number (ℝ)

Missing 

Distinct14
Distinct (%)3.7%
Missing440
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2.7386667
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-06-22T23:34:05.050946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7.3
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4497022
Coefficient of variation (CV)0.89448718
Kurtosis16.656619
Mean2.7386667
Median Absolute Deviation (MAD)1
Skewness3.1269038
Sum1027
Variance6.001041
MonotonicityNot monotonic
2025-06-22T23:34:05.152536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 146
 
17.9%
2 81
 
9.9%
3 57
 
7.0%
4 34
 
4.2%
5 16
 
2.0%
6 15
 
1.8%
8 10
 
1.2%
7 7
 
0.9%
9 3
 
0.4%
10 2
 
0.2%
Other values (4) 4
 
0.5%
(Missing) 440
54.0%
ValueCountFrequency (%)
1 146
17.9%
2 81
9.9%
3 57
 
7.0%
4 34
 
4.2%
5 16
 
2.0%
6 15
 
1.8%
7 7
 
0.9%
8 10
 
1.2%
9 3
 
0.4%
10 2
 
0.2%
ValueCountFrequency (%)
23 1
 
0.1%
17 1
 
0.1%
15 1
 
0.1%
11 1
 
0.1%
10 2
 
0.2%
9 3
 
0.4%
8 10
1.2%
7 7
0.9%
6 15
1.8%
5 16
2.0%

Interactions

2025-06-22T23:33:48.718267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T23:33:36.845212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T23:33:38.056133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T23:33:40.418377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T23:33:48.605553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-22T23:34:05.296276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AdmitWardBMICan you go up and down the stairs without assistance?Counter_PreviousOrmisActivityDo you currently use any walking aids to walk?Do you have downstairs access to a toilet?Do you have stairs at home?Do you live alone?Flag_IsDiagnosedDiabetesFlag_IsDiagnosedHeartRateFlag_IsDiagnosedHypertensionFlag_IsDiagnosedMentalandBehaviourAlcoholFlag_IsDiagnosedObesityFlag_IsDiagnosedSmokerHave you had any falls in the past 12 months?Have you had previous joint replacement surgery?Height CM CORRECTEDHow anxious are you about having joint replacement surgery?How far can you currently walk?How long do you estimate it would take you to stand from a chair, walk 5 steps away from the chair and then return to the chair again?How long do you expect to stay in hospital after your operation?IDLengthOfStayOperationLengthMinutePRIORITY_TYPE_DESCRIPTIONPatientClassificationDescPatientTotalTimePersonEthnicCategoryDescPersonGenderPrimaryProcedureDescProfCarerOnAdmitNameSWFT_LoSSmokerTheatreCodeTheatreNameUnnamed: 5Weight KG CORRECTEDWill there be someone, such as a family member or friend, who can support you during your initial recovery?
AdmitWard1.0000.0000.0130.0000.0370.0000.0000.1040.0340.0000.0720.0000.0270.0470.0850.0000.0000.0000.0000.0000.0990.6000.0000.4470.0000.2050.6030.0000.0000.5700.4360.1870.0000.6170.6170.0000.1020.018
BMI0.0001.0000.0000.0060.0000.0180.0910.1090.0820.0000.0000.0000.4790.0000.1190.083-0.1030.0300.0720.0000.0270.0780.0180.1310.0670.0000.0090.0000.0330.0000.0000.0000.0000.0250.025-0.0790.7720.141
Can you go up and down the stairs without assistance?0.0130.0001.0000.0400.3370.0730.3110.1530.0210.0000.0000.0000.0000.0910.1830.0330.0000.1570.3080.2120.1760.0350.1840.0440.0000.0000.0000.1530.1110.2280.0000.1650.1120.1740.1740.0000.0000.199
Counter_PreviousOrmisActivity0.0000.0060.0401.0000.0000.0000.0400.0000.0000.1540.0680.1540.0000.0520.1350.060-0.0980.0510.0000.0000.0300.0420.0200.0150.0000.0000.0310.0000.0000.0000.0000.0510.0720.0000.000-0.136-0.0300.000
Do you currently use any walking aids to walk?0.0370.0000.3370.0001.0000.0290.1320.1380.1850.0060.0190.0060.1810.0000.2170.0940.0000.0940.3950.2890.1700.0000.1610.0800.0000.0000.0610.0770.1750.1980.0990.2140.0000.0420.0420.0000.0000.129
Do you have downstairs access to a toilet?0.0000.0180.0730.0000.0291.0000.1970.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0800.0560.0000.0000.0000.0000.0000.0720.0000.0000.1270.0000.0200.0000.0000.0000.0000.000
Do you have stairs at home?0.0000.0910.3110.0400.1320.1971.0000.1480.0000.0000.0460.0000.0000.0000.0790.0000.0000.0000.0950.0540.0000.0000.1140.0390.0000.0000.0000.0000.0000.1150.0000.1320.0000.1080.1080.0000.1180.110
Do you live alone?0.1040.1090.1530.0000.1380.0590.1481.0000.0110.0000.0470.0000.0000.0870.0390.0950.0590.0000.0900.0000.1670.1380.1480.0000.0000.0000.0000.0000.1460.0610.1060.1950.1000.0490.0490.0680.0540.609
Flag_IsDiagnosedDiabetes0.0340.0820.0210.0000.1850.0000.0000.0111.0000.0000.2360.0000.2390.0000.0680.0000.0000.1070.2130.1620.1040.1210.2080.0000.0000.0000.0000.0700.0360.0000.0000.1170.0000.0320.0320.1700.0000.139
Flag_IsDiagnosedHeartRate0.0000.0000.0000.1540.0060.0000.0000.0000.0001.0000.0000.8740.0000.0000.0000.0000.0000.0000.0870.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0300.0000.0000.000
Flag_IsDiagnosedHypertension0.0720.0000.0000.0680.0190.0000.0460.0470.2360.0001.0000.0000.1070.0000.0380.0800.0000.0000.1090.0400.0360.0570.0740.1320.0000.0000.0480.0000.1110.1740.0000.1290.0000.0000.0000.0490.0000.078
Flag_IsDiagnosedMentalandBehaviourAlcohol0.0000.0000.0000.1540.0060.0000.0000.0000.0000.8740.0001.0000.0000.0000.0000.0000.0000.0000.0870.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0300.0000.0000.000
Flag_IsDiagnosedObesity0.0270.4790.0000.0000.1810.0000.0000.0000.2390.0000.1070.0001.0000.0000.1280.0000.0970.1300.1640.1650.1250.1570.0880.0840.0000.0000.0000.0660.0670.0000.0580.0520.0000.0000.0000.1300.3110.136
Flag_IsDiagnosedSmoker0.0470.0000.0910.0520.0000.0000.0000.0870.0000.0000.0000.0000.0001.0000.0000.0000.0000.0240.0550.0140.0250.0000.0000.0970.0000.0000.1710.0000.0000.0560.1550.0770.6910.0930.0930.0000.0000.009
Have you had any falls in the past 12 months?0.0850.1190.1830.1350.2170.0000.0790.0390.0680.0000.0380.0000.1280.0001.0000.0410.0000.1120.2620.1780.1380.1160.1470.0000.0000.0000.0550.0000.0450.0000.0770.1380.0280.1440.1440.0470.0380.104
Have you had previous joint replacement surgery?0.0000.0830.0330.0600.0940.0000.0000.0950.0000.0000.0800.0000.0000.0000.0411.0000.0250.1610.0970.0000.0990.0000.0540.1110.0340.0200.1090.0000.0640.2900.0000.0380.0000.0700.0700.0240.0000.000
Height CM CORRECTED0.000-0.1030.000-0.0980.0000.0000.0000.0590.0000.0000.0000.0000.0970.0000.0000.0251.0000.0150.0540.0400.000-0.015-0.122-0.0360.0000.0000.0630.0000.1160.1440.0000.0000.0000.0400.0401.0000.4950.000
How anxious are you about having joint replacement surgery?0.0000.0300.1570.0510.0940.0240.0000.0000.1070.0000.0000.0000.1300.0240.1120.1610.0151.0000.1530.1330.1650.0410.0340.0000.0000.0000.0000.0000.2330.0570.0000.0000.0000.0000.0000.0420.0000.027
How far can you currently walk?0.0000.0720.3080.0000.3950.0000.0950.0900.2130.0870.1090.0870.1640.0550.2620.0970.0540.1531.0000.3380.2760.0640.1360.0680.0000.0000.0350.0000.2050.1640.0000.2040.0630.1050.1050.0000.0000.038
How long do you estimate it would take you to stand from a chair, walk 5 steps away from the chair and then return to the chair again?0.0000.0000.2120.0000.2890.0000.0540.0000.1620.0180.0400.0180.1650.0140.1780.0000.0400.1330.3381.0000.1570.0250.0630.0000.0210.0720.0000.0000.1430.0000.0560.1060.0170.0000.0000.0000.0000.083
How long do you expect to stay in hospital after your operation?0.0990.0270.1760.0300.1700.0800.0000.1670.1040.0000.0360.0000.1250.0250.1380.0990.0000.1650.2760.1571.0000.1450.0700.0790.0570.1040.0980.0000.2080.1440.0090.1510.0660.1370.1370.0000.0810.119
ID0.6000.0780.0350.0420.0000.0560.0000.1380.1210.0000.0570.0000.1570.0000.1160.000-0.0150.0410.0640.0250.1451.000-0.138-0.0730.1060.181-0.2730.0540.0390.0000.0960.2240.0280.2740.274-0.0180.0230.094
LengthOfStay0.0000.0180.1840.0200.1610.0000.1140.1480.2080.0000.0740.0000.0880.0000.1470.054-0.1220.0340.1360.0630.070-0.1381.0000.1900.0000.0000.1250.2280.0730.3510.0000.1840.0000.0000.000-0.104-0.0650.175
OperationLengthMinute0.4470.1310.0440.0150.0800.0000.0390.0000.0000.0000.1320.0000.0840.0970.0000.111-0.0360.0000.0680.0000.079-0.0730.1901.0000.0000.1880.2090.0000.0000.4500.2010.0390.0540.3740.374-0.0080.0950.000
PRIORITY_TYPE_DESCRIPTION0.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0210.0570.1060.0000.0001.0000.1470.0780.0000.0000.1830.0610.0000.0000.0000.0000.0000.0000.000
PatientClassificationDesc0.2050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0720.1040.1810.0000.1880.1471.0000.1950.0000.0000.1690.1280.1420.0000.1760.1760.0000.0800.000
PatientTotalTime0.6030.0090.0000.0310.0610.0000.0000.0000.0000.0000.0480.0000.0000.1710.0550.1090.0630.0000.0350.0000.098-0.2730.1250.2090.0780.1951.0000.0000.0000.5490.3720.1290.0940.4210.4210.0850.0570.000
PersonEthnicCategoryDesc0.0000.0000.1530.0000.0770.0720.0000.0000.0700.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0540.2280.0000.0000.0000.0001.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.000
PersonGender0.0000.0330.1110.0000.1750.0000.0000.1460.0360.0000.1110.0000.0670.0000.0450.0640.1160.2330.2050.1430.2080.0390.0730.0000.0000.0000.0000.0591.0000.0950.1500.1100.0000.0480.0480.0000.1500.063
PrimaryProcedureDesc0.5700.0000.2280.0000.1980.0000.1150.0610.0000.0000.1740.0000.0000.0560.0000.2900.1440.0570.1640.0000.1440.0000.3510.4500.1830.1690.5490.0000.0951.0000.4500.0620.0320.4630.4630.0000.0000.000
ProfCarerOnAdmitName0.4360.0000.0000.0000.0990.1270.0000.1060.0000.0000.0000.0000.0580.1550.0770.0000.0000.0000.0000.0560.0090.0960.0000.2010.0610.1280.3720.0000.1500.4501.0000.0000.0550.3400.3400.0000.0720.000
SWFT_LoS0.1870.0000.1650.0510.2140.0000.1320.1950.1170.0000.1290.0000.0520.0770.1380.0380.0000.0000.2040.1060.1510.2240.1840.0390.0000.1420.1290.0000.1100.0620.0001.0000.0710.0920.0920.0580.0000.173
Smoker0.0000.0000.1120.0720.0000.0200.0000.1000.0000.0000.0000.0000.0000.6910.0280.0000.0000.0000.0630.0170.0660.0280.0000.0540.0000.0000.0940.0000.0000.0320.0550.0711.0000.0000.0000.0000.0000.007
TheatreCode0.6170.0250.1740.0000.0420.0000.1080.0490.0320.0300.0000.0300.0000.0930.1440.0700.0400.0000.1050.0000.1370.2740.0000.3740.0000.1760.4210.0000.0480.4630.3400.0920.0001.0001.0000.0000.0000.000
TheatreName0.6170.0250.1740.0000.0420.0000.1080.0490.0320.0300.0000.0300.0000.0930.1440.0700.0400.0000.1050.0000.1370.2740.0000.3740.0000.1760.4210.0000.0480.4630.3400.0920.0001.0001.0000.0000.0000.000
Unnamed: 50.000-0.0790.000-0.1360.0000.0000.0000.0680.1700.0000.0490.0000.1300.0000.0470.0241.0000.0420.0000.0000.000-0.018-0.104-0.0080.0000.0000.0850.0000.0000.0000.0000.0580.0000.0000.0001.0000.4940.000
Weight KG CORRECTED0.1020.7720.000-0.0300.0000.0000.1180.0540.0000.0000.0000.0000.3110.0000.0380.0000.4950.0000.0000.0000.0810.023-0.0650.0950.0000.0800.0570.0000.1500.0000.0720.0000.0000.0000.0000.4941.0000.078
Will there be someone, such as a family member or friend, who can support you during your initial recovery?0.0180.1410.1990.0000.1290.0000.1100.6090.1390.0000.0780.0000.1360.0090.1040.0000.0000.0270.0380.0830.1190.0940.1750.0000.0000.0000.0000.0000.0630.0000.0000.1730.0070.0000.0000.0000.0781.000

Missing values

2025-06-22T23:33:50.387726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-22T23:33:50.837460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-22T23:33:51.712340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDAge (years):Height (please input in centimeters)Weight (please input in kilograms)Height CM CORRECTEDUnnamed: 5Weight KG CORRECTEDBMISmokerDo you live alone?Will there be someone, such as a family member or friend, who can support you during your initial recovery?Do you have stairs at home?Can you go up and down the stairs without assistance?Do you have downstairs access to a toilet?How far can you currently walk?Do you currently use any walking aids to walk?Have you had any falls in the past 12 months?How long do you expect to stay in hospital after your operation?How anxious are you about having joint replacement surgery?Have you had previous joint replacement surgery?How long do you estimate it would take you to stand from a chair, walk 5 steps away from the chair and then return to the chair again?PersonGenderPersonBirthDateAdmissionDateTimeDischargeDateTimeProfCarerOnAdmitNamePrimaryProcedureCodePrimaryProcedureDescTheatreCodeTheatreNameAdmitWardOperationStartDateTimeOperationEndDateTimeOperationLengthMinutePatientTotalTimeLengthOfStayFlag_IsDiagnosedDiabetesFlag_IsDiagnosedHypertensionFlag_IsDiagnosedSmokerFlag_IsDiagnosedHeartRateFlag_IsDiagnosedMentalandBehaviourAlcoholFlag_IsDiagnosedObesityFlag_IsDiagnosedDisorderOfBrainSWFT_LoSPRIORITY_TYPE_DESCRIPTIONPersonEthnicCategoryDescPostcodePatientClassificationDescCounter_PreviousOrmisActivity
016417896.6kg178.01.7896.630.488575NoNoYes, including overnightYesYesYesMore than a mileNoNoGo home the same dayNot at allNoMore than 15 secondsMale1956-08-28 00:00:002021-07-08 07:00:002021-07-09 15:30:07ShahW381Primary total prosthetic replacement of hip joint not using cementTHE 6THEATRE 6GREVILLE WARD2021-07-08 11:27:002021-07-08 12:38:0071.0120.01.0NNNNNNN0.0RoutineBritishCV37 0AAOrdinary admission.2.0
125017085170.01.7085.029.411765NoNoYes, including overnightYesYesNoLess than a mileNoNoGo home the same dayModeratelyNoLess than 15 secondsFemale1970-10-16 00:00:002021-07-09 07:00:002021-07-11 11:49:36RobertsonW401Primary total prosthetic replacement of knee joint using cementTHE5Theatre 5GREVILLE WARD2021-07-09 12:23:002021-07-09 13:48:0085.0180.02.0NNNNNNN1.0RoutineBritishCV35 8PAOrdinary admission.3.0
2359150cm92kg150.01.5092.040.888889NoYesYes, in the daytime onlyYesYesYesLess than a mileNoNo1 nightMildlyYesLess than 15 secondsFemale1962-01-09 00:00:002021-07-12 07:00:002021-07-13 18:00:20FaisalW381Primary total prosthetic replacement of hip joint not using cementTHE5Theatre 5GREVILLE WARD2021-07-12 13:22:002021-07-12 14:55:0093.0120.01.0NNNNNYN1.0RoutineBritishCV3 5GAOrdinary admission.NaN
346316473164.01.6473.027.141582NoNoYes, including overnightYesYesYesLess than a mileYes - I use a walking stick or crutchNo1 nightVeryNoLess than 15 secondsFemale1957-11-20 00:00:002021-07-16 07:00:002021-07-18 18:00:39ShahW381Primary total prosthetic replacement of hip joint not using cementTHE 6THEATRE 6GREVILLE WARD2021-07-16 14:31:002021-07-16 15:55:0084.0120.02.0NYNNNNN1.0RoutineBritishB95 5HAOrdinary admission.NaN
4575182.899.2182.01.8299.229.948074NoNoYes, including overnightYesYesYesMore than a mileNoYes1 nightModeratelyNoLess than 15 secondsMale1946-04-07 00:00:002021-07-20 07:00:002021-07-21 19:33:50RobertsonW401Primary total prosthetic replacement of knee joint using cementTHE 6THEATRE 6GREVILLE WARD2021-07-20 11:35:002021-07-20 13:05:0090.0135.01.0NYNNNNN1.0RoutineBritishCV31 3LAOrdinary admission.1.0
567116865168.01.6865.023.030045NoNoYes, including overnightYesYesYesLess than a mileYes - I use a walking stick or crutchNo1 nightVeryNoLess than 15 secondsMale1949-08-05 00:00:002021-07-22 11:00:002021-07-24 16:43:29ShahW381Primary total prosthetic replacement of hip joint not using cementTHE5Theatre 5GREVILLE WARD2021-07-22 15:32:002021-07-22 17:18:00106.0120.02.0NYNNNNN1.0RoutineBritishCV47 1GAOrdinary admission.NaN
677616070160.01.6070.027.343750NoNoYes, including overnightYesYesYesLess than a mileYes - I use a walking stick or crutchNo2 nightsModeratelyNoLess than 15 secondsMale1944-12-21 00:00:002021-07-23 07:00:002021-07-28 18:05:45RobertsonW401Primary total prosthetic replacement of knee joint using cementTHE3Theatre 3GREVILLE WARD2021-07-23 15:39:002021-07-23 16:53:0074.0135.05.0YYNNNNN2.0RoutineBritishCV47 0JAOrdinary admission.3.0
7882162.5681.4162.01.6281.431.016613NoYesYes, in the daytime onlyNoNoYesIn the house onlyYes - I use a wheeled walkerNo3 or more nightsVeryNoMore than 15 secondsFemale1938-10-23 00:00:002021-07-23 07:00:002021-07-26 17:00:02ShahW381Primary total prosthetic replacement of hip joint not using cementTHE 6THEATRE 6GREVILLE WARD2021-07-23 11:35:002021-07-23 13:39:00124.0120.03.0NNNNNNN3.0RoutineBritishCV37 8XAOrdinary admission.NaN
897819089190.01.9089.024.653740NoNoYes, including overnightYesYesNoIn the house onlyYes - I use a walking stick or crutchYes2 nightsModeratelyNoMore than 15 secondsMale1943-06-14 00:00:002021-07-24 07:00:002021-07-26 18:45:02ShahW381Primary total prosthetic replacement of hip joint not using cementTHE 6THEATRE 6GREVILLE WARD2021-07-24 13:29:002021-07-24 14:55:0086.0130.02.0NYNNNNN0.0UrgentBritishCV8 2FAOrdinary admission.2.0
910505ft 6inch98kg167.01.6798.035.139302YesYesYes, including overnightYesYesNoLess than a mileNoYes1 nightMildlyYesMore than 15 secondsFemale1971-03-02 00:00:002021-07-27 07:00:002021-07-28 15:16:39RobertsonW581Primary resurfacing arthroplasty of jointTHE 6THEATRE 6GREVILLE WARD2021-07-27 10:04:002021-07-27 11:26:0082.0194.01.0NYYNNYN1.0RoutineBritishCV35 0AAOrdinary admission.4.0
IDAge (years):Height (please input in centimeters)Weight (please input in kilograms)Height CM CORRECTEDUnnamed: 5Weight KG CORRECTEDBMISmokerDo you live alone?Will there be someone, such as a family member or friend, who can support you during your initial recovery?Do you have stairs at home?Can you go up and down the stairs without assistance?Do you have downstairs access to a toilet?How far can you currently walk?Do you currently use any walking aids to walk?Have you had any falls in the past 12 months?How long do you expect to stay in hospital after your operation?How anxious are you about having joint replacement surgery?Have you had previous joint replacement surgery?How long do you estimate it would take you to stand from a chair, walk 5 steps away from the chair and then return to the chair again?PersonGenderPersonBirthDateAdmissionDateTimeDischargeDateTimeProfCarerOnAdmitNamePrimaryProcedureCodePrimaryProcedureDescTheatreCodeTheatreNameAdmitWardOperationStartDateTimeOperationEndDateTimeOperationLengthMinutePatientTotalTimeLengthOfStayFlag_IsDiagnosedDiabetesFlag_IsDiagnosedHypertensionFlag_IsDiagnosedSmokerFlag_IsDiagnosedHeartRateFlag_IsDiagnosedMentalandBehaviourAlcoholFlag_IsDiagnosedObesityFlag_IsDiagnosedDisorderOfBrainSWFT_LoSPRIORITY_TYPE_DESCRIPTIONPersonEthnicCategoryDescPostcodePatientClassificationDescCounter_PreviousOrmisActivity
8058068715272152.00NaN72.031.2NoYesYes, in the daytime onlyNoNoYesLess than a mileYes - I use a walking stick or crutchNo1 nightMildlyYesLess than 15 secondsFemale1333645253.29166745255.740081ShahW401Primary total prosthetic replacement of knee joint using cementTHE 6THEATRE 6THOMAS WARD45253.48680645253.55625100.0120.02.0NYNNNNN1.0RoutineBritishB93 8BAOrdinary admission.NaN
806807585.9125175.26NaN125.040.7YesNoYes, including overnightYesYesNoLess than a mileNoYes1 nightVeryYesLess than 15 secondsFemale2380645307.29166745308.833704RobertsonW581Primary resurfacing arthroplasty of jointTHE 6THEATRE 6THOMAS WARD45307.51666745307.595139113.0150.01.0NNYNNNN1.0RoutineNot statedCV21 1PAOrdinary admission.3.0
80780859186119186.00NaN119.034.4NoNoYes, including overnightYesYesNoIn the house onlyYes - I use a walking stick or crutchYes2 nightsVeryYesMore than 15 secondsMale2345545329.29166745334.438079ShahW401Primary total prosthetic replacement of knee joint using cementTHE 6THEATRE 6THOMAS WARD45329.66180645329.744444119.0120.05.0YYNNNYN1.0RoutineBritishCV4 9EAOrdinary admission.1.0
808809631.892180.00NaN92.028.4YesNoYes, including overnightYesYesYesMore than a mileNoNo1 nightModeratelyNoLess than 15 secondsMale2199945376.29166745377.759745RobertsonW401Primary total prosthetic replacement of knee joint using cementTHE 6THEATRE 6THOMAS WARD45376.39166745376.479167126.0120.01.0NNNNNNN1.0RoutineBritishCV6 2DAOrdinary admission.NaN
8098107115670156.00NaN70.028.8NoNoYes, including overnightYesYesNoMore than a mileNoNo1 nightMildlyNoLess than 15 secondsFemale1925145495.29166745496.663981FaisalNaNNaNVAN1VANGUARD ONETHOMAS WARD45495.56736145495.62583.0120.01.0NNNNNNN1.0RoutineBritishHR5 3BAOrdinary admission.NaN
810811571100100.00NaN100.0100.0NoNoYes, including overnightYesYesYesMore than a mileNoNo1 nightModeratelyNoLess than 15 secondsMale2443445376.29166745377.760069Ud-DinW381Primary total prosthetic replacement of hip joint not using cementTHE4Theatre 4THOMAS WARD45376.67152845376.72430676.0120.01.0NNNNNNN1.0RoutineBritishLD3 0AAOrdinary admission.NaN
8118127317486174.00NaN86.028.4NoYesNoYesYesYesLess than a mileNoYes2 nightsMildlyNoLess than 15 secondsMale1839045356.29166745357.666759RobertsonW401Primary total prosthetic replacement of knee joint using cementVAN1VANGUARD ONETHOMAS WARD45356.57545356.63888992.0120.01.0NYNNNNN1.0RoutineBritishB94 5SAOrdinary admission.NaN
8128138215059150.00NaN59.026.2NoNoYes, including overnightYesNoYesIn the house onlyYes - I use a walking frameNo2 nightsVeryYesMore than 15 secondsFemale1755045471.29166745473.877095FaisalW381Primary total prosthetic replacement of hip joint not using cementTHE4Theatre 4THOMAS WARD45471.52083345471.58402891.0120.02.0NYNNNNN1.0RoutineBritishB93 0PAOrdinary admission.NaN
8138147667cm5967.00NaN59.0131.4NoYesYes, in the daytime onlyYesYesNoMore than a mileNoNo1 nightMildlyNoLess than 15 secondsFemale1755045471.29166745473.877095FaisalW381Primary total prosthetic replacement of hip joint not using cementTHE4Theatre 4THOMAS WARD45471.52083345471.58402891.0120.02.0NYNNNNN1.0RoutineBritishB93 0PAOrdinary admission.NaN
81481558174120174.00NaN120.039.6NoNoYes, including overnightYesYesYesLess than a mileNoNo1 nightVeryNoLess than 15 secondsFemale2413545338.29166745340.766308Ud-DinW401Primary total prosthetic replacement of knee joint using cementTHE 6THEATRE 6THOMAS WARD45338.56597245338.62222281.0120.02.0NYNNNYN1.0RoutineBritishCV4 9NADay case admission.NaN